Humans ; Cardiologists ; Neoplasms/drug therapy* ; Medical Oncology ; Heart ; Oncologists

OBJECTIVE: To investigate the incidence rate and risk factors for metabolic bone disease of prematurity (MBDP) in very low birth weight/extremely low birth weight (VLBW/ELBW) infants. METHODS: The medical data of 61 786 neonates from multiple centers of China between September 1, 2013 and August 31, 2016 were retrospectively investigated, including 504 VLBW/ELBW preterm infants who met the inclusion criteria. Among the 504 infants, 108 infants diagnosed with MBDP were enrolled as the MBDP group and the remaining 396 infants were enrolled as the non-MBDP group. The two groups were compared in terms of general information of mothers and preterm infants, major diseases during hospitalization, nutritional support strategies, and other treatment conditions. The multivariate logistic regression analysis was used to investigate the risk factors for MBDP. RESULTS: The incidence rate of MBDP was 19.4% (88/452) in VLBW preterm infants and 38.5% (20/52) in ELBW preterm infants. The incidence rate of MBDP was 21.7% in preterm infants with a gestational age of < 32 weeks and 45.5% in those with a gestational age of < 28 weeks. The univariate analysis showed that compared with the non-MBDP group, the MBDP group had significantly lower gestational age and birth weight, a significantly longer length of hospital stay, and a significantly higher incidence rate of extrauterine growth retardation ( CONCLUSIONS A lower gestational age, hypocalcemia, extrauterine growth retardation at discharge, and neonatal sepsis may be associated an increased risk of MBDP in VLBW/ELBW preterm infants. It is necessary to strengthen perinatal healthcare, avoid premature delivery, improve the awareness of the prevention and treatment of MBDP among neonatal pediatricians, and adopt positive and reasonable nutrition strategies and comprehensive management measures for preterm infants.
Birth Weight ; Bone Diseases, Metabolic/etiology* ; China/epidemiology* ; Female ; Humans ; Infant ; Infant, Extremely Low Birth Weight ; Infant, Newborn ; Infant, Premature ; Infant, Very Low Birth Weight ; Pregnancy ; Retrospective Studies ; Risk Factors

Objective:To establish a disease risk prediction model for the newborn screening system of inherited metabolic diseases by artificial intelligence technology.Methods:This was a retrospectively study. Newborn screening data ( n=5 907 547) from February 2010 to May 2019 from 31 hospitals in China and verified data ( n=3 028) from 34 hospitals of the same period were collected to establish the artificial intelligence model for the prediction of inherited metabolic diseases in neonates. The validity of the artificial intelligence disease risk prediction model was verified by 360 814 newborns ' screening data from January 2018 to September 2018 through a single-blind experiment. The effectiveness of the artificial intelligence disease risk prediction model was verified by comparing the detection rate of clinically confirmed cases, the positive rate of initial screening and the positive predictive value between the clinicians and the artificial intelligence prediction model of inherited metabolic diseases. Results:A total of 3 665 697 newborns ' screening data were collected including 3 019 cases ' positive data to establish the 16 artificial intelligence models for 32 inherited metabolic diseases. The single-blind experiment ( n=360 814) showed that 45 clinically diagnosed infants were detected by both artificial intelligence model and clinicians. A total of 2 684 cases were positive in tandem mass spectrometry screening and 1 694 cases were with high risk in artificial intelligence prediction model of inherited metabolic diseases, with the positive rates of tandem 0.74% (2 684/360 814)and 0.46% (1 694/360 814), respectively. Compared to clinicians, the positive rate of newborns was reduced by 36.89% (990/2 684) after the application of the artificial intelligence model, and the positive predictive values of clinicians and artificial intelligence prediction model of inherited metabolic diseases were 1.68% (45/2 684) and 2.66% (45/1 694) respectively. Conclusion:An accurate, fast, and the lower false positive rate auxiliary diagnosis system for neonatal inherited metabolic diseases by artificial intelligence technology has been established, which may have an important clinical value.

OBJECTIVE: To study the percentage of the measured values of the main pulmonary ventilation function parameters in their predicted values based on Zapletal equation among healthy children aged 5-14 years in Kunming, China, and to provide a basis for accurate judgment of pulmonary ventilation function in clinical practice. METHODS: A total of 702 healthy children aged 5-14 years (352 boys and 350 girls) from Kunming were enrolled. The Jaeger spirometer was used to measure the nine indices:forced vital capacity (FVC), forced expiratory volume in one second (FEV1), ratio of forced expiratory volume in one second to forced vital capacity (FEV1/FVC), maximal mid-expiratory flow (MMEF), forced expiratory flow at 25% of forced vital capacity (FEF25), forced expiratory flow at 50% of forced vital capacity (FEF50), forced expiratory flow at 75% of forced vital capacity (FEF75), peak expiratory flow (PEF), and maximal voluntary ventilation (MVV). The values obtained from the Zapletal equation of predicted values provided by the spirometer were used as the predicted values of children, and the percentage of measured values in predicted values was calculated. RESULTS: In the 702 children, the percentages of the measured values of the main pulmonary ventilation function parameters PEF, FVC, FEV1, FEV1/FVC, and MVV in their predicted values fluctuated from 102% to 114%, 94% to 108%, 98% to 113%, 98% to 107%, and 141% to 183% respectively. As for the main airway velocity parameters, the percentages of the measured values of FEF25, FEF50, FEF75, and MMEF in their predicted values fluctuated from 98% to 116%, 85% to 102%, 71% to 98%, and 83% to 100% respectively. The percentages of the measured values of PEF, FVC, FEV1, FEV1/FVC, MVV, FEF25, FEF50, FEF75, and MMEF in their predicted values had the lower limits of normal of 88.2%, 88.4%, 92.0%, 94.4%, 118.5%, 82.9%, 70.0%, 62.1%, and 70.1% respectively. CONCLUSIONS There are differences between pulmonary ventilation function parameter levels and normal values provided by Zapletal equation in healthy children aged 5-14 years in Kunming. As for the pulmonary ventilation function parameters of PEF, FVC, FEV, FEV1/FVC, MVV, FEF25, FEF50, FEF75, and MMEF in these children, the lower limits of normal of measured values in predicted values may be determined as 88.2%, 88.4%, 92.0%, 94.4%, 118.5%, 82.9%, 70.0%, 62.1%, and 70.1% respectively.
Adolescent ; Child ; Child, Preschool ; China ; Female ; Forced Expiratory Volume ; Humans ; Lung/physiology* ; Male ; Pulmonary Ventilation ; Reference Values ; Vital Capacity

Objective:To analyze the changes of soil microbial community structure before and after planting Gastrodia elata in different producing areas，and to investigate the response of soil microorganisms to the planting of G. elata. Method:ITS and 16S rDNA high-throughput sequencing technologies were used to detect fungal and bacterial community compositions in the soil，including the soil without planting G. elata（CK1，CK2），the soil around G. elata（GE1，GE2）before harvesting, and the soil around the rhizomorph of Armillaria（AGE1，AGE2） in Dafang, Guizhou and Jinzhai, Anhui respectively. Result:Principal component analysis (PCA) showed that the soil microorganisms changed significantly after G. elata planting as compared with the control soil. The sequencing results showed that the planting of G. elata increased the OTUs number of fungi and bacteria. As compared with the control soil，the diversity and abundance of fungal and bacterial communities showed an increase trend after the cultivation of G. elata in soil of Dafang, Guizhou， such changes of fungal communities were not significant, but the abundance of soil bacteria communities increased in Jinzhai, Anhui as compared with the control soil. The abundance of genera Ilyonectria and Nitrospira increased，while genera Russula decreased significantly both in the soil of Guizhou and Anhui. Furthermore，the abundance of Fusarium and Mortierella increased significantly in the soil of Dafang, Guizhou. Conclusion:The soil microorganisms were out of balance after planting of G. elata, and the abundance of pathogenic microorganisms such as Ilyonectria and Fusarium increased，which may be related to the plant diseases and insect pests of G. elata.